Education in Nepal: What are we missing?
Big picture
The education sector in Nepal is still a black box. The government invests significantly to expand access to education, although the budget share towards education has been consistently declining (from 17% in 2067/68 to a mere 11.1% in 2073/74). Public universities and colleges have become a political battlefield rather than productive knowledge enterprise, compelling tens of thousands of students to leave the country every year to pursue their further education abroad.
Partisan politics, bureaucratic challenges and outside interference have rendered institutional governance unable to hold exams and declare results on time let alone commit to improving education quality and learning environment. Nobody precisely knows which academic practices need to be controlled or encouraged. The divide between government and private schools is exacerbating. The S.L.C. pass percent of private schools has consistently crossed the 90% mark in recent years while that of community schools has average around 40%. Yet, policymakers are essentially swatting flies with a hammer and doing little to remedy the situation.
With increasing calls for education reform, evidence-based understanding of inputs and outputs of learner success can be instrumental. As business intelligence acts as the decision foundation in the corporate sector, learning analytics can be the basis for effective, positive change in education.
Educational institutions and ministries have stockpiled data on grades, attendance, textbook purchase and use, test scores, cafeteria meals, etc. But not much has been done to use that information to enhance learning except to store aggregated data as institutional statistics. Opportunities for data gathering, dissemination and analysis are constantly overlooked.
When students, teachers and administrators have the information to make decisions, students excel. Accurate measures of each student’s performance and progress can help identify which students need extra support and why they are lagging behind. Data can be useful in designing personalized learning in-and-outside the classroom environment, taking into account students unique interests and prior abilities. Using in-process evaluation, learning analytics could help create adaptive and competency-based course contents appropriate to the education needs of individual learners. Identifying how successful learners work will help schools encourage similar habits and best practices to improve overall student performance. Only with the proper knowledge of what works should schools decide on how to spend their resources to better student outcomes and satisfaction.
The focus of education policy studies in Nepal till date has been limited to boosting enrolment and reducing attrition in schools. Looking at early signals (or the lack of it) from students provide early indications of dropping out. Even simple analyses of data including that on teacher effectiveness, classroom interactions or assignment submission patterns could indicate what kind of preventative action is needed. Timely interventions can reduce dropout rates significantly.
What data science can’t do however is capture the softer aspects of learning, such as encouragement from class teacher, intrinsic motivation, students’ social interactions outside the classroom, etc. Considering this as a caveat, it is imperative to understand the role of learning analytics in providing structure for accountability, self-evaluation and improvement of educational institutions in Nepal.
Data needs
Well-informed reforms require right information at the right time to make important decisions. Institutions need to be clear about what students must achieve and have the data to foresee that they are on the track to success. Various types of data on students, teachers, learning processes and education institutions can come together to paint a full picture necessary to support the education goals of students and educators.
Student data consists of academic information such as prior school performance, grade level, and courses enrolled. More importantly, scores from assignments, quizzes and exams in various subjects provide the scale for measuring performance and progress. Standard measures include scores in math, science and written and verbal English. It is also of interest to keep performance records of close peers in academic and social networks of students.
Data on use of cafeteria meals/ nutrition levels as well as individual’s physical and emotional health status; class attendance, disciplinary actions and extra-curricular involvements/ leadership abilities; and demographic information on age, race, gender, economic status as well as parental education levels and socioeconomic background can be equally indicative of children’s learning outcomes.
Teacher performance measures like absence rates, competency and education level attained, previous professional experience, and student evaluations are vital. So is classroom data on size and composition, physical environment, and types of accessible learning resources.
In addition to student and teacher abilities, teaching and learning processes influence learning outcomes. Lesson plans, teaching methods (interactive or instructional/ use of PowerPoint or white boards, etc.), frequency and difficulty level of assessments, methods of assessments (e.g. multiple choice or essay questions), regular feedbacks from teachers and student evaluation of the course can provide valuable insights on why or why not students are performing well.
On a broader level, school data, such as attrition, completion and graduation rates, educational and non-educational HR and infrastructure expenditures, availability of social and psychological student support systems and parents surveys can provide important inputs as to how educational institutions can build a suitable learning environment for its students.
Future Prospects
Not too far from meeting the Sustainable Development Goals for education, quality remains an elusive pursuit in Nepal and across the majority of developing countries. Vast amount of intellectual and policy effort is being spent internationally to pinpoint how education systems can be reformed to provide quality learning for all. Researchers have left no stones unturned in measuring the effectiveness of wide varieties of policy action - the impacts of pedagogical interventions, introduction of technology and computers in classrooms, incentivized hiring of teachers and even availability of student desks in classrooms have been documented in great detail. From the perspective of a policymaker, it is a matter of testing and adopting solutions that are most relevant to the Nepalese political economy of education. But to do that, evidence-based planning and implementation are quintessential ingredients.
The education sector in Nepal is still a black box. The government invests significantly to expand access to education, although the budget share towards education has been consistently declining (from 17% in 2067/68 to a mere 11.1% in 2073/74). Public universities and colleges have become a political battlefield rather than productive knowledge enterprise, compelling tens of thousands of students to leave the country every year to pursue their further education abroad.
Partisan politics, bureaucratic challenges and outside interference have rendered institutional governance unable to hold exams and declare results on time let alone commit to improving education quality and learning environment. Nobody precisely knows which academic practices need to be controlled or encouraged. The divide between government and private schools is exacerbating. The S.L.C. pass percent of private schools has consistently crossed the 90% mark in recent years while that of community schools has average around 40%. Yet, policymakers are essentially swatting flies with a hammer and doing little to remedy the situation.
With increasing calls for education reform, evidence-based understanding of inputs and outputs of learner success can be instrumental. As business intelligence acts as the decision foundation in the corporate sector, learning analytics can be the basis for effective, positive change in education.
Educational institutions and ministries have stockpiled data on grades, attendance, textbook purchase and use, test scores, cafeteria meals, etc. But not much has been done to use that information to enhance learning except to store aggregated data as institutional statistics. Opportunities for data gathering, dissemination and analysis are constantly overlooked.
When students, teachers and administrators have the information to make decisions, students excel. Accurate measures of each student’s performance and progress can help identify which students need extra support and why they are lagging behind. Data can be useful in designing personalized learning in-and-outside the classroom environment, taking into account students unique interests and prior abilities. Using in-process evaluation, learning analytics could help create adaptive and competency-based course contents appropriate to the education needs of individual learners. Identifying how successful learners work will help schools encourage similar habits and best practices to improve overall student performance. Only with the proper knowledge of what works should schools decide on how to spend their resources to better student outcomes and satisfaction.
The focus of education policy studies in Nepal till date has been limited to boosting enrolment and reducing attrition in schools. Looking at early signals (or the lack of it) from students provide early indications of dropping out. Even simple analyses of data including that on teacher effectiveness, classroom interactions or assignment submission patterns could indicate what kind of preventative action is needed. Timely interventions can reduce dropout rates significantly.
What data science can’t do however is capture the softer aspects of learning, such as encouragement from class teacher, intrinsic motivation, students’ social interactions outside the classroom, etc. Considering this as a caveat, it is imperative to understand the role of learning analytics in providing structure for accountability, self-evaluation and improvement of educational institutions in Nepal.
Data needs
Well-informed reforms require right information at the right time to make important decisions. Institutions need to be clear about what students must achieve and have the data to foresee that they are on the track to success. Various types of data on students, teachers, learning processes and education institutions can come together to paint a full picture necessary to support the education goals of students and educators.
Student data consists of academic information such as prior school performance, grade level, and courses enrolled. More importantly, scores from assignments, quizzes and exams in various subjects provide the scale for measuring performance and progress. Standard measures include scores in math, science and written and verbal English. It is also of interest to keep performance records of close peers in academic and social networks of students.
Data on use of cafeteria meals/ nutrition levels as well as individual’s physical and emotional health status; class attendance, disciplinary actions and extra-curricular involvements/ leadership abilities; and demographic information on age, race, gender, economic status as well as parental education levels and socioeconomic background can be equally indicative of children’s learning outcomes.
Teacher performance measures like absence rates, competency and education level attained, previous professional experience, and student evaluations are vital. So is classroom data on size and composition, physical environment, and types of accessible learning resources.
In addition to student and teacher abilities, teaching and learning processes influence learning outcomes. Lesson plans, teaching methods (interactive or instructional/ use of PowerPoint or white boards, etc.), frequency and difficulty level of assessments, methods of assessments (e.g. multiple choice or essay questions), regular feedbacks from teachers and student evaluation of the course can provide valuable insights on why or why not students are performing well.
On a broader level, school data, such as attrition, completion and graduation rates, educational and non-educational HR and infrastructure expenditures, availability of social and psychological student support systems and parents surveys can provide important inputs as to how educational institutions can build a suitable learning environment for its students.
Future Prospects
Not too far from meeting the Sustainable Development Goals for education, quality remains an elusive pursuit in Nepal and across the majority of developing countries. Vast amount of intellectual and policy effort is being spent internationally to pinpoint how education systems can be reformed to provide quality learning for all. Researchers have left no stones unturned in measuring the effectiveness of wide varieties of policy action - the impacts of pedagogical interventions, introduction of technology and computers in classrooms, incentivized hiring of teachers and even availability of student desks in classrooms have been documented in great detail. From the perspective of a policymaker, it is a matter of testing and adopting solutions that are most relevant to the Nepalese political economy of education. But to do that, evidence-based planning and implementation are quintessential ingredients.
data analytics FOR DEVELOPMENT
Today, data analytics – not limited to data collection, dissemination, analysis and communication – is a crucial stimulus for productivity growth. From algorithmic high frequency trading to disaster mitigation using satellite imagery and remote sensing, data science has proven to be a valuable tool in every aspect of life in advanced and emerging economies, and will become indispensable in the coming years. Although data technology has gradually trickled down to the developing world, there is limited ability to coordinate and make the best use of it. Now is the time to change this.
Data informs decision-making and thus has the tremendous ability to positively influence outcomes in public policy, non-profit and social entrepreneurship, etc. Innovation in collection methods and analysis of data from the field can provide local and international organizations a clearer picture of the effectiveness of their development and relief efforts without having to wait for subjective, circumstantial field evidence. Crowd-sourced geospatial data was vital to getting assistance to where it was needed most during the post-earthquake crisis in Nepal. The massive amounts of data collected from satellites, mobile phones as well as social media all guided the emergency responses. Analysis of data assessing initial conditions and subsequent changes from interventions can radically change the way meaningful impact is measured.
Data analytics allows for more transparency and accountability and can facilitate open dissemination of official statistics – a movement already gaining momentum – to reform the convention of information hoarding embedded in Nepalese institutions. Proper communication can encourage the rather frustrated external users, analysts and commentators to form their independent opinions and fully participate in the policymaking process in a variety of areas. Leveraging data technology to engage community members in collaborative projects with local government and NGOs and INGOs can provide a solid foundation for participatory democracy.
Analytics can generate gains from synergy by expediting inter-organizational coordination by transforming the asymmetric, disorganized and outdated means of communication within and between private sector, academia, government departments, non-government agencies and their end-beneficiaries. Research activities and plans can be better synchronized and resources can be focused on shared problems with the common objective of developing effective technological solutions. Well-connected scientific and intellectual enterprise across disciplines, organizations and sectors can boost innovation.
Extracting insights from data is an important tool to promptly and accurately measure firm performance, and monitor and influence business outcomes in every industry including manufacturing, financial services, communication, pharmaceutical, health care and more. Active and systematic use of data sharpens strategies and increases efficiency, as it enables companies to integrate information across supply chain, understand consumer behavior, recognize trends, make real-time decisions and customize solutions to particular issues that arise due to rapid change of pace in customer demands as well as in the macro-economic environment. Predictive analysis makes businesses agile and proactive whether the goal is to drive revenue, speed market delivery, increase market share, optimize workforce, or realize other operational improvements. Data-intensive companies are in an unparalleled position to figure out what works, what does not, and why.
Data science has larger implications than improving organizational functioning. Lagging behind in this realm may seem affordable compared to myriad of other political and socioeconomic challenges Nepal faces. Paradoxically, it is countries like ours that are eluded by technological progress where the potential of data is endless in addressing the fundamental obstacles that inhibit growth and integration with the modern world. Technology and data literacy are much more immediate necessities than we realize to mitigate the effects of an exacerbating digital and economic divide within the country, especially considering the fact that only 10% of Nepalese school children have access to computers.
Data analytics could be a game changer but the infrastructure and capability to operationalize it is insufficient to unlock its true value for Nepal’s development. Long-term targeted investment for building statistical capacity – both human and technical – is needed to build a strong foundation for a data-driven culture. Well-equipped staff with analytical skills, availability of reliable data and state-of-the-art data management and analytical systems could encourage evidence-based planning and implementation.
But the biggest challenge of evolving from a knowing culture that largely relies on heuristics to a learning culture driven by data is not the cost. In the beginning, it is inertia and the lack of imagination. What is needed is a steady progression towards a much more learning-oriented, dynamic mindset that is necessary for the sustainable wellbeing of any institution and the broader economy of which it is an integral part.
In short, businesses, social/non-profit organizations and governments should move fast to make the most of what data science has to offer. It is essential to understand that data science is not a modern panacea for Nepal’s problems. Having said that, its diffusion nevertheless offers countless opportunities to utilize powerful tools to overcome longstanding development barriers.
